In this paper, a mechanism of adaptive width adjustment based on immunological vaccination is proposed for the evolutionary training of RBF neural networks. Inspired by the vaccination process of the natural immune system, the algorithm implements an individual-orientated adaptation of the width in training stages to optimize the potential solutions, therefore reinforces the evolutionary capability and efficiency. A two-layer genotype-coding scheme, which enables a simultaneous evolution of network structure and parameters, is presented to achieve a compact and consistent-in-form solution. The proposed learning strategy is tested on several benchmark problems and results demonstrate promise.
Three different learning rnethods for RBF networks and their combinations are preserited. Standard gradient learning, three-step algorithm with unsupervised part, and evolutionary algorithm are introduced. Their performance is compared on two benchmark problerns: Two spirals and Iris plants. The results show that the three-step learning is usually the fastest, while the gradient learning achieves better precision. The cornbination of these two approaches gives the best results.